57 citations · 122 across the 9 of their papers we have counts for
6 papers · 1 filter
Semantic Membership Inference Attack against Large Language Models
Hamid Mozaffari, Virendra J. Marathe
Membership Inference Attacks (MIAs) determine whether a specific data point was included in the training set of a target model. In this paper, we introduce the Semantic Membership…
FedPerm: Private and Robust Federated Learning by Parameter Permutation
Hamid Mozaffari, Virendra J. Marathe, Dave Dice
Federated Learning (FL) is a distributed learning paradigm that enables mutually untrusting clients to collaboratively train a common machine learning model. Client data privacy is…
Subject Granular Differential Privacy in Federated Learning
Virendra J. Marathe, Pallika Kanani, Daniel W. Peterson +1
This paper considers subject level privacy in the FL setting, where a subject is an individual whose private information is embodied by several data items either confined within a…
Subject Membership Inference Attacks in Federated Learning
Anshuman Suri, Pallika Kanani, Virendra J. Marathe +1
Privacy attacks on Machine Learning (ML) models often focus on inferring the existence of particular data points in the training data. However, what the adversary really wants to k…
Private Cross-Silo Federated Learning for Extracting Vaccine Adverse Event Mentions
Pallika Kanani, Virendra J. Marathe, Daniel Peterson +2
Federated Learning (FL) is quickly becoming a goto distributed training paradigm for users to jointly train a global model without physically sharing their data. Users can indirect…
Private Federated Learning with Domain Adaptation
Daniel Peterson, Pallika Kanani, Virendra J. Marathe
Federated Learning (FL) is a distributed machine learning (ML) paradigm that enables multiple parties to jointly re-train a shared model without sharing their data with any other p…